Adam Foroughi, Applovin CEO: Surviving a 92% Drawdown, Ads as ML 1.0 & the $50B Game Ad Market

AppLovin scaled into a massive ad platform without early venture funding by tapping into the mobile gaming ecosystem, which attracts over a billion daily active adults and heads of households. Facilitating monetization for game developers generates tens of billions of dollars in annual ad spend across the industry. This enormous user engagement creates a robust environment for establishing intent and driving continuous revenue growth.

Early digital advertising served as the foundational implementation of machine learning, effectively functioning as ML 1.0 long before modern large language models gained widespread prominence. Although recommendation systems and large language models possess structural differences, their underlying research trajectories frequently cross-pollinate between domains. Furthermore, advertising platforms offer the unique advantage of immediately translating predictive machine learning outputs into quantifiable business outcomes.

Digital advertising bifurcates into bottom-of-funnel search intent, where users research products they already intend to buy, and upper-funnel discovery, where ads introduce novel products. While search-based ads largely compete with search engines without generating net-new economic expansion, discovery ads drive genuine economic growth by inspiring unprompted consumer purchases. This discovery model generates both dopamine-driven consumer enjoyment and significant macroeconomic expansion.

After going public during the COVID boom, AppLovin experienced a severe market downturn that compressed its valuation from a $28 billion market cap down to $3.8 billion despite generating substantial annual EBITDA. Recognizing that public market pricing stemmed from temporary supply-demand imbalances and a lack of blue-chip research, CEO Adam Foroughi halted traditional investor relations and deployed cash reserves into aggressive share buybacks. By retiring a significant portion of outstanding shares at depressed prices, the company transformed a demoralizing drawdown into massive long-term value.

AppLovin revitalized its financial and operational trajectory by upgrading its core advertising algorithm from an early regression model (ML 1.0) to an advanced deep learning architecture (ML 2.0). This technical transition significantly improved advertiser return on investment and drove rapid year-over-year revenue expansion. When leadership finally re-engaged with public market investors once performance scaled, the market rapidly repriced the company upward.

Regulatory shifts and platform-level privacy controls, such as Apple's tracking restrictions, require ad tech companies to adapt by grouping users less precisely rather than abandoning targeted advertising. While stricter privacy rules initially create headwinds, clear regulatory frameworks allow technology to adapt and continue delivering relevant ads that consumers prefer over random spam. Ultimately, users want relevant product recommendations when engaging with content, and modern deep learning networks operate effectively within privacy constraints.

AppLovin initially acquired and operated its own mobile game studios to solve a critical data scarcity problem when training its first deep learning model. Because third-party game developers were initially reluctant to share proprietary training data with an external platform, owning studios allowed AppLovin to seed its models with necessary training data. Once the algorithms proved successful and third-party partners gained confidence, the company divested those gaming studios to focus purely on ad technology.

Smaller companies can successfully compete against massive technology giants by maintaining extreme operational leanness, fostering subject matter expertise, and moving much faster than larger competitors. Maintaining a paranoid, continuous-improvement mindset prevents complacency despite past successes, allowing nimble teams to carve out dominant market positions. By focusing deeply on a specific niche and executing efficiently, leaner organizations can outperform heavily resourced competitors.